Suppose we make a linear regression model on each of the 10 folds with the same number of features (say 2 for simplification) We will therefore have 10 sets of coefficients with the optimized values of the parameters with each of the metrics (for example R2).
In the end what model do we retain? (the best, the average values obtained for each parameter, the model that obtained the average R2?) Ouvrir dans Google Traduction
Generally, if you are using folds, the final prediction is calculated as an average of all predictions obtained using the models based on folds. Linear regression is a special case of ML methods, where averaging the predictions gives the same results as using a model of averaged parameters. So, regarding your question, in your way of using folds and models, the proper parameters of the final model are the average values obtained for each parameter.
